Speed prediction from mobile sensors using cellular phone‐based traffic data

Basyoni, Yarah; Abbas, Hazem M.; Talaat, Hoda; Dimeery, Ibrahim El

doi:10.1049/iet-its.2016.0279

Cited by 7 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Abdulhai et al [42,43] suggested application of GA for selection of an optimal number of upstream and downstream spatial locations (as well as for other parameters of their neural network-based forecasting model). Recently GA were applied for spatial [44][45][46] and temporal [47] feature selection. The PSO approach was applied by Chan et al [48,49] and recently combined with GA by Zheng et al [50].…”

Section: Class 3: Wrapper Feature Selection Methodsmentioning

confidence: 99%

Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review

Pavlyuk

2019

Eur. Transp. Res. Rev.

View full text Add to dashboard Cite

A spatiotemporal approach that simultaneously utilises both spatial and temporal relationships is gaining scientific interest in the field of traffic flow forecasting. Accurate identification of the spatiotemporal structure (dependencies amongst traffic flows in space and time) plays a critical role in modern traffic forecasting methodologies, and recent developments of data-driven feature selection and extraction methods allow the identification of complex relationships. This paper systematically reviews studies that apply feature selection and extraction methods for spatiotemporal traffic forecasting. The reviewed bibliographic database includes 211 publications and covers the period from early 1984 to March 2018. A synthesis of bibliographic sources clarifies the advantages and disadvantages of different feature selection and extraction methods for learning the spatiotemporal structure and discovers trends in their applications. We conclude that there is a clear need for development of comprehensive guidelines for selecting appropriate spatiotemporal feature selection and extraction methods for urban traffic forecasting.

show abstract

Section: Class 3: Wrapper Feature Selection Methodsmentioning

confidence: 99%

Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review

Pavlyuk

2019

Eur. Transp. Res. Rev.

View full text Add to dashboard Cite

show abstract

“…A gradient-based approach with a distributed algorithm that allows for data exchange among sensors using location information was proposed by Habibi in [15], which is not covered in our own research scope as well. A speed prediction model to track the sensors was investigated by Basyoni et al in [16], while a zoning-based tracking technique over Wi-Fi and using belief function was implemented by Alshamaa in [17], to properly ascertain the target area.…”

Section: Preliminaries and Related Workmentioning

confidence: 99%

Novel MA-VFBC Based Deployment of Obstacle-Avoiding Scattered Sensors for Region-of-Interest Incidence Monitoring

2020

View full text Add to dashboard Cite

The deployment of mobile and adaptive virtual force barrier coverage (MA-VFBC) classification scheme using a mobile emergency response and command interface (MERCI) platform that is functionally implemented to track and report incidences and consequent collateral damages to infrastructures within a region of interest (ROI) is proposed. Considering the enormous use of the global positioning system (GPS) devices for location data-gathering and processing, and its inherent limitations, the proposed GUIbased MA-VFBC platform is implemented using self-deploying and obstacle-avoiding scattered mobile sensor nodes. The GPS service is kept as alternatives, since only initial coordinates from where the deployed sensor starts to move and the maximum boundary location of the target location is considered. The practical experimentation work appraises the use and feel of the (MERCI) platform when integrated with the proposed novel MA-VFBC path-tracking classification schemes, while the simulation work investigates evident real-time system reliability issues as direction of node deployment with path distances, system computation time and system overheads in the presence of dissimilar multiple obstacles. INDEX TERMS Emergency response, mobile and adaptive virtual force barrier coverage (MA-VFBC), MERCI, obstacle-avoiding sensor nodes, ROI incidence assessment.

show abstract

“…This paper applied the NARX with a closed-loop network architecture. While no feedback data is available in the model, we used an average value of raw data that was obtained from the interoperable simulation of C2 and C. 35 This method is used because it is suitable for representing the properties of a nonlinear dynamic system, which the communication system has, and has been commonly used for system identification in various methods, such as the autoregressive method, with or without exogenous input. 36 Therefore, to configure the atomic model in this way, the NARX is suitable for expressing the properties of the discrete event dynamic system.…”

Section: Proposed Communication Discrete Event Dynamic Surrogate Modelmentioning

confidence: 99%

Section: Discrete Event Dynamic Surrogate Modelmentioning

confidence: 99%

Machine learning-based discrete event dynamic surrogate model of communication systems for simulating the command, control, and communication system of systems

2018

View full text Add to dashboard Cite

Command and control (C2) and communication are at the heart of successful military operations in network-centric warfare. Interoperable simulation of a C2 system model and a communication (C) system model may be employed to interactively analyze their detailed behaviors. However, such simulation would be inefficient in simulation time for analysis of combat effectiveness of the C2 model against possible input combinations while considering the communication effect in combat operations. This study proposes a discrete event dynamic surrogate model (DEDSM) for the C model, which would be integrated with the C2 model and simulated. The proposed integrated simulation reduces execution time markedly in analysis of combat effectiveness without sacrificing the accuracy reflecting the communication effect. We hypothesize the DEDSM as a probabilistic priority queuing model whose semantics is expressed in a discrete event systems specification model with some characteristic functions unknown. The unknown functions are identified by machine learning with a data set generated by interoperable simulation of the C2 and C models. The case study with the command, control, and communication system of systems first validates the proposed approach through an equivalence test between the interoperable simulation and the proposed one. It then compares the simulation execution times and the number of events exchanged between the two simulations.

show abstract

Speed prediction from mobile sensors using cellular phone‐based traffic data

Cited by 7 publications

References 18 publications

Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review

Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review

Novel MA-VFBC Based Deployment of Obstacle-Avoiding Scattered Sensors for Region-of-Interest Incidence Monitoring

Machine learning-based discrete event dynamic surrogate model of communication systems for simulating the command, control, and communication system of systems

Contact Info

Product

Resources

About